Clinical tissue samples are formalin fixed and paraffin embedded (FFPE) to preserve the samples for archival purposes. Such FFPE tissue samples include samples from tumors, which often include heavy admixtures of normal cells and tumor cells. FFPE tissue samples are a vast resource of clinically annotated samples with patient follow-up data including diagnostic and therapeutic outcomes. As such, these samples represent highly desirable and informative materials for the application of high definition genomics that could improve patient management and provide a molecular basis for the selection of personalized therapeutics. The availability of well annotated archived samples represents a highly favorable resource to study the basis of therapeutic responses and the clinical history of human cancers. Fragment size of DNA template, however, is very low for extracted FFPE material.
The recent development of whole exome and whole genome technologies provides an unparalleled opportunity for advances in improved treatment and diagnosis for patients with cancer. One major limitation to the use of routinely prepared FFPE tissue samples to date is the highly variable quality of the DNA extracted from samples of interest. In addition, high-resolution molecular analyses of biomaterials from human specimens are highly dependent on the cellular composition of the specimens. For example, a high degree of surrounding normal cells in a tumor tissue can make it difficult to isolate a sufficient number of neoplastic cells for high definition analysis of cancer genomes.
The poor quality of DNA from FFPE samples and the highly heterogeneous tumor content of tumor samples are barriers to effectively utilizing the huge number of FFPE samples available for study. The use of FFPE is currently very limited for next-generation sequencing because of sample degradation and heavy admixtures of normal and tumor cells. Current efforts in next-generation sequencing are targeting high number reads (e.g., >100×) to overcome tissue heterogeneity. Increasing read number, however, only exacerbates errors associated with poor quality samples.
Recent studies have described various methods to interrogate FFPE samples with advanced array and sequencing technologies. These typically select samples exceeding a threshold for tumor cell content based on histological methods, such as, evaluation of H&E stained slides, use of macrodissection, or laser capture approaches prior to analysis. Once selected, samples are typically extracted in bulk using various protocols consisting of dewaxing, removal of cross links, and subsequent DNA extraction and purification. Array and sequencing analyses of these samples typically require relatively large amounts of starting material to achieve appropriate signal to noise levels. However, many samples, notably tumors arising in solid tissues, exhibit high degrees of tissue heterogeneity with varied admixtures of reactive stroma, inflammatory cells and necrosis in immediate contact with tumor cells.
Furthermore, it is well established that biopsies frequently contain multiple clonal populations of neoplastic cells that cannot be distinguished on the basis of morphology alone. Consequently, histology-based methods cannot readily distinguish whether aberrations in a tumor are present in a single cancer genome or if they are distributed in multiple clonal populations. Thus, current approaches for the analyses of cancer genomes using FFPE samples are limited and lacking in their ability to determine the clinical context of each patient's tumor.
A similar problem is seen in clinical diagnostic settings. While the issue of degraded DNA due to tissue fixing is not present, the difficulty in providing high-resolution genetic profiling still exists. One approach is passage of tumor biopsies in tissue culture or in xenografts. These methods apply selective pressures on the complex mixtures of cells and clones present in a patient sample, are time-consuming, labor intensive, and are not amenable to rapid deployment in most clinical settings. Consequently, the number of xenografts successfully grown varies from site-to-site, and the biological complexity and clinical context of the patient sample may not be reflected in the final processed sample.
Flow cytometry-based cell sorters can select, objectively measure, and sort individual particles such as cells or nuclei using desired features objectively defined by fluorescent and light scattering parameters in a flow stream. Recent advances in this technology provide high throughput flow rates and the detection of relatively rare events in dilute admixed samples, enabling the application of flow cytometry to in vivo high definition analyses of human cancers. Different reports have shown that tumor cells can be efficiently sorted from FFPE samples using DNA content based assays. Sorted FFPE samples have been used for PCR based assays and SNP arrays. However, these approaches typically have limited resolution in the number of genes and loci interrogated. Furthermore, the use of SNP arrays requires the use of reduced complexity samples.
The combination of flow sorting and genomic analyses has been recently used for the enrichment of pancreas carcinoma cells and to study the clonal composition of primary breast tumors. These studies, however, relied on extensive bioinformatic analyses, platform-specific sample preparations, or relatively large amounts of input material to achieve an acceptable signal-to-noise ratio in their genome analyses. Further, the methods of such studies become even more unfeasible if applied to FFPE samples, where the added problem of DNA degradation is present. A need exists for an improved system and method of identifying aberrations in cell and tissue samples, especially in complex, variable carcinoma genomes derived from FFPE. One object of the present invention is to provide a system and method with improved resolution to facilitate the differentiation and identification of these aberrations. See, e.g., Ibrahim, S F, et al. (2007), “Flow cytometry and cell sorting.” Adv Biochem Eng Biotechnol 106:19-39; Navin N, et al. (2010), “Inferring tumor progression from genomic heterogeneity.” Genome Res 20:68-80; and, Boyd Z S, et al. (2009), “A tumor sorting protocol that enables enrichment of pancreatic adenocarcinoma cells and facilitation of genetic analyses.” J Mol Diagn 11:290-297, which are each herein incorporated by reference for all purposes.